73. Set Matrix Zeroes

矩阵置零算法
本文介绍了一种在原地修改矩阵的算法,当矩阵中的某个元素为0时,将其所在行和列的所有元素设置为0。文章提供了一种使用常量空间的解决方案,详细解释了如何利用矩阵的第一行和第一列作为标记,避免使用额外的空间。

Given a m x n matrix, if an element is 0, set its entire row and column to 0. Do it in-place.

Example 1:

Input: 
[
  [1,1,1],
  [1,0,1],
  [1,1,1]
]
Output: 
[
  [1,0,1],
  [0,0,0],
  [1,0,1]
]

Example 2:

Input: 
[
  [0,1,2,0],
  [3,4,5,2],
  [1,3,1,5]
]
Output: 
[
  [0,0,0,0],
  [0,4,5,0],
  [0,3,1,0]
]

Follow up:

A straight forward solution using O(mn) space is probably a bad idea.
A simple improvement uses O(m + n) space, but still not the best solution.
Could you devise a constant space solution?

难度:medium

题目:给定m * n的矩阵,如果某一元素为0,则将其所在行及列都设为0。在原矩阵上执行。

一种直接的解法是空间复杂度为O(mn)
一种简单的改进是空间复杂度为O(m + n),但是仍不是最好的。
是否可以用常量空间给出解法。

思路:先统计首行,首列是否含有0。然后用首行首列来记录其它行列。

Runtime: 1 ms, faster than 99.98% of Java online submissions for Set Matrix Zeroes.
Memory Usage: 45.6 MB, less than 0.96% of Java online submissions for Set Matrix Zeroes.

class Solution {
    public void setZeroes(int[][] matrix) {
        int m = matrix.length, n = matrix[0].length;
        int row0 = 1, column0 = 1;
        // first row
        for (int i = 0; i < n; i++) {
            if (0 == matrix[0][i]) {
                row0 = 0;
            }
        }
        // first column
        for (int i = 0; i < m; i++) {
            if (0 == matrix[i][0]) {
                column0 = 0;
            }
        }
        // other rows and columns
        for (int i = 1; i < m; i++) {
            for (int j = 1; j < n; j++) {
                if (0 == matrix[i][j]) {
                    matrix[i][0] = 0;
                    matrix[0][j] = 0;
                }
            }
        }
        // set 0 for other rows and columns
        for (int i = 1; i < m; i++) {
            for (int j = 1; j < n; j++) {
                if (0 == matrix[0][j] || 0 == matrix[i][0]) {
                    matrix[i][j] = 0;
                }
            }
        }
        // set 0 for first row
        for (int i = 0; 0 == row0 && i < n; i++) {
            matrix[0][i] = 0;
        }
        // set 0 for first column
        for (int i = 0; 0 == column0 && i < m; i++) {
            matrix[i][0] = 0;
        }
    }
}
import numpy as np import pandas as pd import matplotlib.pyplot as plt plt.rcParams['font.sans-serif'] = ["SimHei"] # 单使用会使负号显示错误 plt.rcParams['axes.unicode_minus'] = False # 把负号正常显示 # 读取北京房价数据 path = 'data.txt' data = pd.read_csv(path, header=None, names=['房子面积', '房子价格']) print(data.head(10)) print(data.describe()) # 绘制散点图 data.plot(kind='scatter', x='房子面积', y='房子价格') plt.show() def computeCost(X, y, theta): inner = np.power(((X * theta.T) - y), 2) return np.sum(inner) / (2 * len(X)) data.insert(0, 'Ones', 1) cols = data.shape[1] X = data.iloc[:,0:cols-1]#X是所有行,去掉最后一列 y = data.iloc[:,cols-1:cols]#X是所有行,最后一列 print(X.head()) print(y.head()) X = np.matrix(X.values) y = np.matrix(y.values) theta = np.matrix(np.array([0,0])) print(theta) print(X.shape, theta.shape, y.shape) def gradientDescent(X, y, theta, alpha, iters): temp = np.matrix(np.zeros(theta.shape)) parameters = int(theta.ravel().shape[1]) cost = np.zeros(iters) for i in range(iters): error = (X * theta.T) - y for j in range(parameters): term = np.multiply(error, X[:, j]) temp[0, j] = theta[0, j] - ((alpha / len(X)) * np.sum(term)) theta = temp cost[i] = computeCost(X, y, theta) return theta, cost alpha = 0.01 iters = 1000 g, cost = gradientDescent(X, y, theta, alpha, iters) print(g) print(computeCost(X, y, g)) x = np.linspace(data.Population.min(), data.Population.max(), 100) f = g[0, 0] + (g[0, 1] * x) fig, ax = plt.subplots(figsize=(12,8)) ax.plot(x, f, 'r', label='Prediction') ax.scatter(data.Population, data.Profit, label='Traning Data') ax.legend(loc=2) ax.set_xlabel('房子面积') ax.set_ylabel('房子价格') ax.set_title('北京房价拟合曲线图') plt.show()
06-04
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